{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction\n",
    "\n",
    "CausalML is a Python package that provides a suite of uplift modeling and causal inference methods using machine learning algorithms based on recent research. The package currently supports the following methods:\n",
    "\n",
    "Tree-based algorithms\n",
    "* Uplift tree/random forests on KL divergence, Euclidean Distance, and Chi-Square\n",
    "* Uplift tree/random forests on Contextual Treatment Selection\n",
    "\n",
    "Meta-learner algorithms\n",
    "* S-learner\n",
    "* T-learner\n",
    "* X-learner\n",
    "* R-learner\n",
    "\n",
    "In this notebook, we use synthetic data to demonstrate the use of the tree-based algorithms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-20T17:20:10.960638Z",
     "start_time": "2019-12-20T17:20:10.957253Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The sklearn.utils.testing module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.utils. Anything that cannot be imported from sklearn.utils is now part of the private API.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from causalml.dataset import make_uplift_classification\n",
    "from causalml.inference.tree import UpliftRandomForestClassifier\n",
    "from causalml.metrics import plot_gain\n",
    "\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generate synthetic dataset\n",
    "\n",
    "The CausalML package contains various functions to generate synthetic datasets for uplift modeling. Here we generate a classification dataset using the make_uplift_classification() function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-20T17:20:10.998192Z",
     "start_time": "2019-12-20T17:20:10.962810Z"
    }
   },
   "outputs": [],
   "source": [
    "df, x_names = make_uplift_classification()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-20T17:20:11.024259Z",
     "start_time": "2019-12-20T17:20:11.000415Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>treatment_group_key</th>\n",
       "      <th>x1_informative</th>\n",
       "      <th>x2_informative</th>\n",
       "      <th>x3_informative</th>\n",
       "      <th>x4_informative</th>\n",
       "      <th>x5_informative</th>\n",
       "      <th>x6_irrelevant</th>\n",
       "      <th>x7_irrelevant</th>\n",
       "      <th>x8_irrelevant</th>\n",
       "      <th>x9_irrelevant</th>\n",
       "      <th>...</th>\n",
       "      <th>x12_uplift_increase</th>\n",
       "      <th>x13_increase_mix</th>\n",
       "      <th>x14_uplift_increase</th>\n",
       "      <th>x15_uplift_increase</th>\n",
       "      <th>x16_increase_mix</th>\n",
       "      <th>x17_uplift_increase</th>\n",
       "      <th>x18_uplift_increase</th>\n",
       "      <th>x19_increase_mix</th>\n",
       "      <th>conversion</th>\n",
       "      <th>treatment_effect</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>control</td>\n",
       "      <td>-0.542888</td>\n",
       "      <td>1.976361</td>\n",
       "      <td>-0.531359</td>\n",
       "      <td>-2.354211</td>\n",
       "      <td>-0.380629</td>\n",
       "      <td>-2.614321</td>\n",
       "      <td>-0.128893</td>\n",
       "      <td>0.448689</td>\n",
       "      <td>-2.275192</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.315304</td>\n",
       "      <td>0.742654</td>\n",
       "      <td>1.891699</td>\n",
       "      <td>-2.428395</td>\n",
       "      <td>1.541875</td>\n",
       "      <td>-0.817705</td>\n",
       "      <td>-0.610194</td>\n",
       "      <td>-0.591581</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>treatment3</td>\n",
       "      <td>0.258654</td>\n",
       "      <td>0.552412</td>\n",
       "      <td>1.434239</td>\n",
       "      <td>-1.422311</td>\n",
       "      <td>0.089131</td>\n",
       "      <td>0.790293</td>\n",
       "      <td>1.159513</td>\n",
       "      <td>1.578868</td>\n",
       "      <td>0.166540</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.391878</td>\n",
       "      <td>-0.623243</td>\n",
       "      <td>2.443972</td>\n",
       "      <td>-2.889253</td>\n",
       "      <td>2.018585</td>\n",
       "      <td>-1.109296</td>\n",
       "      <td>-0.380362</td>\n",
       "      <td>-1.667606</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>treatment1</td>\n",
       "      <td>1.697012</td>\n",
       "      <td>-2.762600</td>\n",
       "      <td>-0.662874</td>\n",
       "      <td>-1.682340</td>\n",
       "      <td>1.217443</td>\n",
       "      <td>0.837982</td>\n",
       "      <td>1.042981</td>\n",
       "      <td>0.177398</td>\n",
       "      <td>-0.112409</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.132497</td>\n",
       "      <td>1.050179</td>\n",
       "      <td>1.573054</td>\n",
       "      <td>-1.788427</td>\n",
       "      <td>1.341609</td>\n",
       "      <td>-0.749227</td>\n",
       "      <td>-2.091521</td>\n",
       "      <td>-0.471386</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>treatment2</td>\n",
       "      <td>-1.441644</td>\n",
       "      <td>1.823648</td>\n",
       "      <td>0.789423</td>\n",
       "      <td>-0.295398</td>\n",
       "      <td>0.718509</td>\n",
       "      <td>-0.492993</td>\n",
       "      <td>0.947824</td>\n",
       "      <td>-1.307887</td>\n",
       "      <td>0.123340</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.084619</td>\n",
       "      <td>0.058481</td>\n",
       "      <td>1.369439</td>\n",
       "      <td>0.422538</td>\n",
       "      <td>1.087176</td>\n",
       "      <td>-0.966666</td>\n",
       "      <td>-1.785592</td>\n",
       "      <td>-1.268379</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>control</td>\n",
       "      <td>-0.625074</td>\n",
       "      <td>3.002388</td>\n",
       "      <td>-0.096288</td>\n",
       "      <td>1.938235</td>\n",
       "      <td>3.392424</td>\n",
       "      <td>-0.465860</td>\n",
       "      <td>-0.919897</td>\n",
       "      <td>-1.072592</td>\n",
       "      <td>-1.331181</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.403984</td>\n",
       "      <td>0.760430</td>\n",
       "      <td>1.917635</td>\n",
       "      <td>-2.347675</td>\n",
       "      <td>1.560946</td>\n",
       "      <td>-0.833067</td>\n",
       "      <td>-1.407884</td>\n",
       "      <td>-0.781343</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  treatment_group_key  x1_informative  x2_informative  x3_informative  \\\n",
       "0             control       -0.542888        1.976361       -0.531359   \n",
       "1          treatment3        0.258654        0.552412        1.434239   \n",
       "2          treatment1        1.697012       -2.762600       -0.662874   \n",
       "3          treatment2       -1.441644        1.823648        0.789423   \n",
       "4             control       -0.625074        3.002388       -0.096288   \n",
       "\n",
       "   x4_informative  x5_informative  x6_irrelevant  x7_irrelevant  \\\n",
       "0       -2.354211       -0.380629      -2.614321      -0.128893   \n",
       "1       -1.422311        0.089131       0.790293       1.159513   \n",
       "2       -1.682340        1.217443       0.837982       1.042981   \n",
       "3       -0.295398        0.718509      -0.492993       0.947824   \n",
       "4        1.938235        3.392424      -0.465860      -0.919897   \n",
       "\n",
       "   x8_irrelevant  x9_irrelevant  ...  x12_uplift_increase  x13_increase_mix  \\\n",
       "0       0.448689      -2.275192  ...            -1.315304          0.742654   \n",
       "1       1.578868       0.166540  ...            -1.391878         -0.623243   \n",
       "2       0.177398      -0.112409  ...            -1.132497          1.050179   \n",
       "3      -1.307887       0.123340  ...            -2.084619          0.058481   \n",
       "4      -1.072592      -1.331181  ...            -1.403984          0.760430   \n",
       "\n",
       "   x14_uplift_increase  x15_uplift_increase  x16_increase_mix  \\\n",
       "0             1.891699            -2.428395          1.541875   \n",
       "1             2.443972            -2.889253          2.018585   \n",
       "2             1.573054            -1.788427          1.341609   \n",
       "3             1.369439             0.422538          1.087176   \n",
       "4             1.917635            -2.347675          1.560946   \n",
       "\n",
       "   x17_uplift_increase  x18_uplift_increase  x19_increase_mix  conversion  \\\n",
       "0            -0.817705            -0.610194         -0.591581           0   \n",
       "1            -1.109296            -0.380362         -1.667606           0   \n",
       "2            -0.749227            -2.091521         -0.471386           0   \n",
       "3            -0.966666            -1.785592         -1.268379           1   \n",
       "4            -0.833067            -1.407884         -0.781343           0   \n",
       "\n",
       "   treatment_effect  \n",
       "0                 0  \n",
       "1                 0  \n",
       "2                 0  \n",
       "3                 1  \n",
       "4                 0  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-20T17:20:11.069258Z",
     "start_time": "2019-12-20T17:20:11.026453Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>conversion</th>\n",
       "      <th>conversion</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>treatment_group_key</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>control</th>\n",
       "      <td>0.511</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>treatment1</th>\n",
       "      <td>0.514</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>treatment2</th>\n",
       "      <td>0.559</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>treatment3</th>\n",
       "      <td>0.600</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>0.546</td>\n",
       "      <td>4000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          mean       size\n",
       "                    conversion conversion\n",
       "treatment_group_key                      \n",
       "control                  0.511       1000\n",
       "treatment1               0.514       1000\n",
       "treatment2               0.559       1000\n",
       "treatment3               0.600       1000\n",
       "All                      0.546       4000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Look at the conversion rate and sample size in each group\n",
    "df.pivot_table(values='conversion',\n",
    "               index='treatment_group_key',\n",
    "               aggfunc=[np.mean, np.size],\n",
    "               margins=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Run the uplift random forest classifier\n",
    "\n",
    "In this section, we first fit the uplift random forest classifier using training data. We then use the fitted model to make a prediction using testing data. The prediction returns an ndarray in which each column contains the predicted uplift if the unit was in the corresponding treatment group."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-20T17:20:11.076379Z",
     "start_time": "2019-12-20T17:20:11.071002Z"
    }
   },
   "outputs": [],
   "source": [
    "# Split data to training and testing samples for model validation (next section)\n",
    "df_train, df_test = train_test_split(df, test_size=0.2, random_state=111)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-20T17:20:11.081266Z",
     "start_time": "2019-12-20T17:20:11.078310Z"
    }
   },
   "outputs": [],
   "source": [
    "uplift_model = UpliftRandomForestClassifier(control_name='control')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-20T17:20:18.741946Z",
     "start_time": "2019-12-20T17:20:11.083854Z"
    }
   },
   "outputs": [],
   "source": [
    "uplift_model.fit(df_train[x_names].values,\n",
    "                 treatment=df_train['treatment_group_key'].values,\n",
    "                 y=df_train['conversion'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-20T17:20:18.887350Z",
     "start_time": "2019-12-20T17:20:18.743659Z"
    }
   },
   "outputs": [],
   "source": [
    "y_pred = uplift_model.predict(df_test[x_names].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-20T17:20:18.892035Z",
     "start_time": "2019-12-20T17:20:18.888977Z"
    }
   },
   "outputs": [],
   "source": [
    "# Put the predictions to a DataFrame for a neater presentation\n",
    "result = pd.DataFrame(y_pred,\n",
    "                      columns=uplift_model.classes_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create the uplift curve\n",
    "\n",
    "The performance of the model can be evaluated with the help of the [uplift curve](http://proceedings.mlr.press/v67/gutierrez17a/gutierrez17a.pdfc). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create a synthetic population\n",
    "\n",
    "The uplift curve is calculated on a synthetic population that consists of those that were in the control group and those who happened to be in the treatment group recommended by the model. We use the synthetic population to calculate the _actual_ treatment effect within _predicted_ treatment effect quantiles. Because the data is randomized, we have a roughly equal number of treatment and control observations in the predicted quantiles and there is no self selection to treatment groups."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-20T17:20:18.902754Z",
     "start_time": "2019-12-20T17:20:18.894201Z"
    }
   },
   "outputs": [],
   "source": [
    "# If all deltas are negative, assing to control; otherwise assign to the treatment\n",
    "# with the highest delta\n",
    "best_treatment = np.where((result < 0).all(axis=1),\n",
    "                           'control',\n",
    "                           result.idxmax(axis=1))\n",
    "\n",
    "# Create indicator variables for whether a unit happened to have the\n",
    "# recommended treatment or was in the control group\n",
    "actual_is_best = np.where(df_test['treatment_group_key'] == best_treatment, 1, 0)\n",
    "actual_is_control = np.where(df_test['treatment_group_key'] == 'control', 1, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-20T17:20:18.907817Z",
     "start_time": "2019-12-20T17:20:18.904664Z"
    }
   },
   "outputs": [],
   "source": [
    "synthetic = (actual_is_best == 1) | (actual_is_control == 1)\n",
    "synth = result[synthetic]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate the observed treatment effect per predicted treatment effect quantile\n",
    "\n",
    "We use the observed treatment effect to calculate the uplift curve, which answers the question: how much of the total cumulative uplift could we have captured by targeting a subset of the population sorted according to the predicted uplift, from highest to lowest?\n",
    "\n",
    "CausalML has the plot_gain() function which calculates the uplift curve given a DataFrame containing the treatment assignment, observed outcome and the predicted treatment effect."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-20T17:20:18.916662Z",
     "start_time": "2019-12-20T17:20:18.909528Z"
    }
   },
   "outputs": [],
   "source": [
    "auuc_metrics = (synth.assign(is_treated = 1 - actual_is_control[synthetic],\n",
    "                             conversion = df_test.loc[synthetic, 'conversion'].values,\n",
    "                             uplift_tree = synth.max(axis=1))\n",
    "                     .drop(columns=list(uplift_model.classes_)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-20T17:20:19.216604Z",
     "start_time": "2019-12-20T17:20:18.918481Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_gain(auuc_metrics, outcome_col='conversion', treatment_col='is_treated')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (General DS)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
